Factor-augmented regression models with structural change

•We consider a factor-augmented regression model with structural change.•We propose a two-step procedure to estimate the model.•The asymptotic properties of the two-step estimator are investigated.•Monte Carlo simulations show that the two-step estimator performs well in finite sample.

This paper considers a factor-augmented regression model in the presence of structural change. We propose a two-step procedure to estimate the coefficients of explanatory variables. We show that when the number of units (N) and the number of periods (T) are large and comparable, the proposed two-step estimator is T-consistent and has the same limiting distribution as if the unobservable factors were observed. Monte Carlo simulations confirm our theoretical results and show good finite sample performance of the two-step estimator.